ENHANCED PROCESSING OF USER PROFILES USING DATA STRUCTURES SPECIALIZED FOR GRAPHICAL PROCESSING UNITS (GPUs)

ABSTRACT

Disclosed are techniques for processing user profiles using data structures that are specialized for processing by a GPU. More particularly, the disclosed techniques relate to systems and methods for evaluating characteristics of user profiles to determine whether to offload certain user profiles to the GPU for processing or to process the user profiles locally by one or more central processing units (CPUs). Processing user profiles may include comparing the interest tags included in the user profiles with logic trees, for example, logic trees representing marketing campaigns, to identify user profiles that match the campaigns.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority benefit of U.S. Provisional PatentApplication No. 62/929,662, filed on Nov. 1, 2019, the disclosure ofwhich is incorporated by reference herein in its entirety for allpurposes.

TECHNICAL FIELD

The present disclosure generally relates to techniques for processingcomputational tasks involving user profiles using specialized datastructures configured to be processed by a Graphical Processing Unit(GPU). More particularly, the present disclosure relates to techniquesfor identifying certain user profiles to offload to the GPU for parallelprocessing. The present disclosure also relates to GPU-specializedtechniques for using attributes of user profiles (e.g., interest tags)to evaluate campaign logic trees to determine which user profiles matchthe campaign logic trees. Additionally, the present disclosure relatesto GPU-specialized techniques for tracking which attributes of userprofiles are responsible for causing a campaign logic tree to evaluateas “true.”

BACKGROUND

Cloud-based applications can perform certain functions on a plurality ofuser profiles stored in cloud-based networks. For example, cloud-basedapplications can enable users to generate queries for user profiles thatinclude certain user attributes, such as a particular interest or userlocation. The cloud-based applications often rely on central processingunits (CPUs) of cloud-based servers to execute the functions. As thescale and complexity of user profiles increases to big-data levels,however, the cloud-based applications may manage millions of userprofiles. Further, each user profile may include tens of thousands ofdistinct user interests. Thus, performing functions using the CPUs ofcloud-based servers has become computationally burdensome andtechnically challenging.

SUMMARY

Certain aspects and features of the present disclosure relate totechniques for efficiently performing computational tasks using GPUs ina cloud-based network environment. Cloud-based applications can performcertain functionality using data sets of user profiles. For example,functionality can include evaluating attributes (e.g., interest tags) ofuser profiles to determine which user profiles satisfy the logic of amarketing campaign. Performing the functionality can involve executingcode using CPUs local to cloud-based servers that support thecloud-based application. However, the computational efficiency ofperforming tasks using cloud-based applications can be improved byevaluating sets of user profiles in a parallel manner, such as by a GPU.GPUs are configured to perform parallel operations, and thus, GPUsincrease the computational efficiency of performing certainfunctionality or tasks, such as determining which user profiles satisfythe logic of a marketing campaign.

Certain aspects and features of the present disclosure relate totechniques for identifying a set of user profiles to offload from a CPUof a cloud-based server to a GPU for processing in a parallel manner. Insome implementations, a cloud-based application may be configured toperform the functionality of evaluating attributes of a set of userprofiles against the logic of a marketing campaign to identify whichuser profiles match (e.g., satisfy as “true”) the logic of the marketingcampaign. A user profile may include one or more user attributes thatcharacterize an individual user. For example, a user attribute can beany metadata detected from or in association with an interaction betweenthe user and a webpage, mobile webpage, or native application. Further,a user attribute can be represented by an interest tag, which is a valuethat represents an interest of the user associated with the userprofile. An interest tag may be stored in a user profile in response toa website or native application detecting an interaction by the userduring a browsing session. To illustrate and only as a non-limitingexample, if a user operates a user device to load a website relating tocoffee, the website may generate a notification to the cloud-basedapplication indicating that the user is interested in coffee. As aresult, the cloud-based application may generate an interest tagrepresenting an interest in coffee and store that interest tag in theuser's user profile.

Additionally, a marketing campaign may be represented by a campaignlogic tree that includes one or more nodes in a hierarchical treestructure. Each node may represent an operand value or an operator. Forexample, the marketing campaign may be represented by a campaign logictree, as follows: “users who are interested in (coffee AND creamer)”. Inthis example, “coffee” and “creamer” are each operand values and “AND”is the operator. A user profile matches or satisfies this campaign logictree as “true” when the user profile includes an interest tag for“coffee” and a separate interest tag for “creamer.” Conversely, a userprofile that does not include an interest tag for “creamer” does notsatisfy this campaign logic tree because the user profile does notsatisfy the campaign logic tree as “true” (e.g., a result of evaluatingthe campaign logic tree using the user profile including only either“coffee” or “creamer” interest tags would not be “true,” but ratherwould be “false”).

I. Determining When to Offload Processing Tasks to GPU

A cloud-based application may use CPUs local to cloud-based servers toperform the task of evaluating the interest tags of user profiles todetermine whether the interest tags satisfy any campaign logic trees. Insome implementations, the cloud-based application may evaluate one ormore characteristics of a user profile to determine whether that userprofile can be offloaded to a GPU for evaluating with other userprofiles in parallel, instead of being processed by the local CPU. Toillustrate and only as a non-limiting example, the cloud-basedapplication may evaluate the number of interest tags included in theuser profile. If the number of interest tags is above a threshold (e.g.,66 or more interest tags), then the cloud-based application may offloadthat user profile to be processed by the GPU instead of the CPU.Alternatively, if the number of interest tags is equal to or below thethreshold, then the cloud-based application may evaluate the userprofiles against campaign logic trees locally using the CPU of thecloud-based server. It will be appreciated that any characteristic ofthe user profile may be evaluated for determining whether to offload theuser profile to the GPU, and thus, the present disclosure is not limitedto the number of interest tags in the example above.

A GPU is configured with a set of threads. Each thread of the set ofthreads may refer to a scheduled chain of instructions running on a GPUcore. The GPU core can independently run as many as 32 threads at thesame time. A warp is a group of 32 threads that each execute in alock-step manner on the same GPU core. GPUs are thus configured forparallel processing. Further, random memory accesses are computationallyexpensive for GPUs, and thus, storing the user profiles and the campaignlogic trees in specialized data structures enables the user profiles andthe campaign logic trees to be efficiently consumed by the GPU forprocessing. A set of user profiles (e.g., a batch of 256 user profiles)may be split into subsets of user profiles. Each subset of user profilesmay match the warp size (e.g., 32 user profiles, with one user profileper GPU thread). Each thread within a warp may process data relating toa single user. For example, thread #1 of the warp may perform read andwrite requests relating to interest tags included in user profile #1,thread #2 of the warp may perform read and write requests relating tointerest tags included in user profile #2, thread #3 of the warp mayperform read and write requests relating to interest tags included inuser profile #3, and so on.

II. Generating a GPU-Specialized Data Structure to Store User Tags in aManner That is Consumable by the GPU

The cloud-based application can perform pre-processing on the userprofiles to transform the user profiles into specialized data structuresthat are consumable by the GPU. For example, the interest tags of a userprofile can be transformed into a data structure that is specialized forconsumption by the GPU. The pre-processing can include organizing theinterest tags included in each user profile in an interleaved manner tofacilitate the coalescing of read and write requests from the GPUprocessing cores. As an illustrative example, pre-processing the userprofiles can include creating an array of interest tags that are storedin a sorted order, such as a user interest array including[interest_id_1 interest_id_2 interest_id_3 . . . interest_id_n].Additionally, pre-processing the user profiles can include creatinganother array of interleaved user profile information, such as an arrayincluding[interest_id_1_applies_to_user_1_interest_id_1_applies_to_user_2 . . .interest_id_1_applies_to_user_32 interest_id_2_applies_to_user_1interest_id_2_applies_to_user_2 . . . interest_id 2_applies_to_user_32 .. . ]. In some examples, user profiles can include additional datafields (other than interest tags), such as tag occurrence count (e.g., anumber of times the interest tag was created) and tag recency (e.g., anumber of days ago that the interest tag was created). Further, if auser profile includes the same interest tag more than once (e.g., twodifferent websites notify the cloud-based application that a user likeschocolate), then multiple slots of the user interest array may bededicated to that interest tag. Any additional data fields may also beincluded in an array in an interleaved manner.

Organizing the specialized data structure storing interest tags of userprofiles enables each GPU thread in the warp to iterate in lock-stepthrough the interest tags stored in the user interest array (e.g., afirst interest tag stored in the array through the last interest tagstored in the array). For each interest tag stored in the array, the GPUthread may determine whether the user profile (that corresponds to theGPU thread) includes that interest tag. If the user profile includesthat interest tag, then the GPU thread may set a bit corresponding tothat particular interest tag's slot in an array corresponding to theuser profile (e.g., the user interests array) as true. Each GPU threadmay access memory adjacent to the memory associated with the other userprofiles, and as such, the read requests from the GPU are coalesced(e.g., if the GPU reads in one user's interest tag bit, the GPU willlikely already be loading the other interest tags of the other userprofiles in that GPU warp for that particular interest tag).

III. Generating a GPU-Specialized Data Structure to Store the Operandsand Operators of Campaign Logic Trees in a Manner That is Consumable bythe GPU

In certain embodiments, the cloud-based application can performpre-processing on the campaign logic trees to organize the operandvalues and operators of the campaign logic trees into a data structurethat is specialized for consumption by the GPU. For example, positions 1to 5 of the first array may correspond to the operands of a firstcampaign logic tree, positions 6 through 13 may correspond to theoperands of a second campaign logic tree, and so on. The specializeddata structure storing the campaign logic trees may include threearrays. The first array may store each operand's interest tag identifier(ID) (e.g., the first array being used for debugging). The second arraymay store the operators for each campaign logic tree. Additionally, athird array may be defined to store data representing whether the nextdata element to be read during evaluation of the first array and thesecond array (e.g., during evaluation of the campaign logic tree) is anoperand or operator. The first array, second array, and the third arraymay be read-only data structures that contain operands (e.g., integers)in the case of the first array, operators (e.g., unsigned chars) in thecase of the second array, and a bit array of Boolean bits indicatingwhether the next data element to be read during evaluation is an operandor operator in the case of the third array. In some examples, aftercreating a campaign data structure, this campaign data structure may betreated as read-only as the cloud-based application proceeds to evaluatethousands of user profiles.

In certain embodiments, when the GPU retrieves user #1's operand value,the GPU threads of a warp each transmit read requests, which arecoalesced, such that when the GPU threads of the warp retrieve user #1'soperand value, the GPU threads also retrieve user #2's through user#32's operand values in a single interaction. In some implementations,the values of each user's campaign logic tree may be interleaved byuser. As an illustrative non-limiting example, the specialized datastructure storing the operand values of the campaign logic trees mayinclude an array of values represented by [operand_1_value_user_1operand_1_value_user_2 . . . operand_1_value_user_32operand_2_value_user_1 operand_2_value_user_2 . . .operand_2_value_user_32 . . . ]. As another illustrative non-limitingexample, the specialized data structure storing the operators of thecampaign logic trees may include an array of values represented by[operator_1_value_user_1 operator 1_value_user_2 . . .operator_1_value_user_32 operator_2 value_user_1_operator_2_value_user_2. . . operator_2_value_user_32 . . . ].

After the campaign logic trees are stored in the specialized datastructures, when the GPU threads of a warp evaluates the logic trees,the read request for each of the GPU threads in the warp can be readtogether in a single interaction. For example, when the GPU thread foruser #1 reads user #1's operand_value_1, the GPU thread for user #2 mayautomatically load user #2's operand_value_1, and so on through user#32's operand_value_1. The read requests transmitted from the GPUthreads of the warp are coalesced (e.g., grouped together) because thethreads of a warp share a cache line. Additionally, when the GPU threadof a warp reads operand_value_1, which could be as small as a bit, theGPU thread may also read the set of operand_value_2_user_*,operand_value_3_user_*, operand_value_4_user_*, and so forth due toloading of the whole cache line.

IV. The GPU Evaluates User Profiles Against Campaign Logic Trees toIdentify Campaigns That Are Possible Matches

In certain embodiments, the GPU thread of a warp may process a userprofile by evaluating each campaign logic tree against the user interesttags included in the user profile. For example, the GPU thread mayaccess an operator array and the operand value array for each campaignlisted as a “possible match” in an array described above. The operatorarray may include the operators from each campaign logic tree. Theoperand value array may include the operand values from each campaignlogic tree. The operator array and the operand value array are datastructures specialized for processing by the GPU. The operator array andthe operand value array are configured to enable each GPU thread of thewarp to read in lock-step either operator values (in the case of readingfrom the operator array) or operand values (in the case of reading fromthe operand value array) in a single transaction. The GPU then uses theuser interest tags included in a user profile to evaluate the operatorarray and the operand value array to determine whether the campaignlogic tree is a true match. The user interest tags may no longer existat this point. Rather, a GPU thread may access the boolean values of theuser interest tags in the operand_value array.

In certain embodiments, when the cloud-based application evaluates oneor more characteristics of a user profile and determines to offload theuser profile to the GPU for processing, the GPU threads of a warp mayeach initiate a matching protocol to identify campaign logic trees thatmatch (or are satisfied by) the user profile. For example, executing thematching protocol may include transmitting read requests to each of thepre-processed GPU-specialized data structures storing the user profilesand the pre-processed GPU-specialized data structures storing thecampaign logic trees. In some cases, a GPU thread of a warp mayinitialize an operand_values array associated with a particular userprofile to contain only false (0) values. The GPU thread may then writeto the operand_values array (e.g., fill in the operand_values for theuser associated with the GPU thread). For example, the GPU thread mayfill in the operands_value array by performing a lookup of an interestmap array to identify the operand offset(s) associated with a giveninterest tag included in the user profile. An offset value may representa campaign logic tree that includes the interest tag as an operand. Foreach operand offset of a given interest tag included in the userprofile, the GPU thread may set the associated value in theoperand_value to “true” (e.g., to “1”).

As another illustrative example, a GPU thread may read an interest tagincluded in a user profile. The GPU thread may then perform a lookup ofthe interest map array and set. The interest map array may include“coffee” as an interest tag. For each campaign logic tree thatreferences “coffee,” the interest map array may include an offset valuethat corresponds to the campaign logic tree. In this example, theinterest map array may include an entry including “coffee” and operandoffsets 33, 2000, and 3800, to refer to the operands in the threecampaign logic trees that reference “coffee.” The GPU may then set thebits at offsets 33, 2000, and 3800, representing “coffee” included inthe operand_value array of the user profile to “true.”

In other implementations, the GPU thread may read an interest tag of“coffee,” and then the GPU thread may perform a lookup of a campaignlogic tree that includes, for example, the logic of “coffee AND tea.”The GPU thread may determine that the campaign logic tree may be markedin a separate array as a “possible match” because the interest tag of“coffee” included in the user profile matches the operand value of“coffee” in the campaign logic tree. At this stage, the GPU thread maynot be evaluating the entire campaign logic tree to determine whetherthe user profile satisfies the entire logic tree, but rather, may bedetermining whether or not the campaign logic tree is a possible match.A campaign logic tree is a possible match when the campaign logic treeincludes an operand value that is also an interest tag of a userprofile.

In certain embodiments, when a GPU thread retrieves an interest tag fromthe user interest array, the GPU thread then performs the computationtask of determining whether the retrieved interest tag is included inthe user profile that corresponds to the GPU thread. If the retrievedinterest tag is included in the user profile corresponding to the GPUthread, then the GPU thread may write a value of “true” to an arrayindicating that the user profile includes the retrieved interest tag.Further, when one interest tag is retrieved, the remaining threads ofthe warp (e.g., the GPU threads corresponding to remaining user #2through user #32) may also be determining whether the retrieved interesttag is included in the user profiles of user #2 through user profile#32.

In certain embodiments, the retrieved interest tag may be associatedwith other interest tags. For example, the GPU thread may retrieveinterest tag #123456. Further, the GPU thread may perform a lookup of amap linking interest tags to other interest tags. As an illustrativeexample, interest tag #123456 may be associated withoperand_value_offset_5, operand_value_offset_450, andoperand_value_offset_98765. The GPU thread may perform a lookup thatidentifies other associated interest tags. The lookup performed by theGPU may be a random memory access. Random memory accesses may becomputationally expensive. Since the 32 user profiles (e.g., the otherGPU threads of a warp) would be performing the lookup of an interest tagon interest tag 123456 at the same time, the GPU threads only have toperform that lookup once.

V. Flagging Campaign Logic Trees as “Possible Matches”

In certain embodiments, the GPU thread that corresponds to a userprofile may re-use the lookup that was previously performed (anddescribed above with respect to the interest map array) for eachinterest tag included in the user profile. The GPU thread may re-use thevalues received from performing the lookup to determine the operandoffset values for that interest tag. Then, the GPU thread may perform alookup of a campaign index for each operand offset value in another map(e.g., an array of integers). The GPU thread may then use thecampaign_index as the offset into a campaign relevance array thatindicates, for each interest tag included in the user profile, whichcampaign logic trees include that interest tag as an operand value. TheGPU may then set the value in that campaign relevance array to “true”(e.g., “1”) to flag the campaign logic tree that is associated with theoffset value included in the operand_offset array.

As an illustrative example, each thread of a warp of a GPU can retrievean interest tag from the user interest array. For every interest tagretrieved, the thread can identify the operand values of variouscampaign logic trees that match the interest tag (e.g., using aninterest map array, which maps interest tags to offset operand values ofcampaign logic trees). For each operand that matches the interest tag,the thread can mark that operand as true by setting a bit in anotherarray to “1” for example. Additionally, the thread can mark the campaignlogic tree that references that operand as a “possible match” by settinga bit to “1” in yet another array. For every campaign logic tree markedas a “possible match,” the thread can evaluate the logic of the campaignlogic tree to determine if the logic has been satisfied as “true.” Ifthe logic has been satisfied as “true,” then the thread sets another bitin another array to indicate that the campaign logic tree has beensatisfied.

In certain embodiments, for each interest tag, after a GPU thread hasdetermined a list of the campaign logic trees that include the interesttag, the GPU may track the list of campaign logic trees by setting avalue in an array associated with the user profile for that GPU thread.The value that is set by the GPU may be set for each campaign logic treethat includes an interest tag that is also included in the user profile.The set value in the array may represent an indication that the campaignlogic tree that corresponds to the set value includes a given interesttag. As an illustrative example, the GPU thread may set values in anarray, as follows: [1 0 1 1 0 0 0], which represents an ordered set ofseven campaign logic trees. The first campaign logic tree in the orderedset corresponds to a value of “1,” indicating that the first campaignlogic tree references an interest of the user. The second campaign logictree in the ordered set corresponds to a value of “0,” and thus, thesecond campaign logic tree does not reference any of the user'sinterests. The third campaign logic tree in the ordered set correspondsto a value of “1,” and thus, the third campaign logic tree doesreference at least one of the user's interests. The fourth campaignlogic tree in the ordered set corresponds to a value of “1,” and thus,the fourth campaign logic tree references the user's interest, and soon, for the fifth, sixth, and seventh campaign logic trees.

VI. Evaluating Campaigns and Marking the Campaigns as a Match

In certain embodiments, to determine whether a campaign logic tree issatisfied by a user profile, each GPU thread of a warp iterativelyevaluates each operator based on the operand values that precede theoperator. In some implementations, the operand values and operators maybe stored in reverse-polish notation, thereby simplifying the evaluationof the campaign logic tree. Further, in some implementations, each GPUthread of a warp checks a bit array representing whether the campaignlogic tree is a possible match to ensure that the GPU thread is onlyevaluating campaign logic trees for which a value indicating “true” wasset. In some implementations, the GPU threads of a warp evaluate operandvalues of the operand value array and operators of the operator array inlock-step. That is, if GPU thread #3 is reading operand_value_789, then,the other GPU threads of the warp (e.g., threads #1, #2, and #4 through#32) may also be reading their respective operand_value_789 because thespecialized data structures enable the GPU threads of a warp to coalesceread and write requests.

After each campaign logic tree is evaluated using the appropriate bitsfrom the operand_values array, the GPU thread may determine whether anyone of the operators were satisfied as “true.” For example, if theinterest tag of a user profile is “coffee” and the campaign logic treeis “coffee OR tea,” then the user profile satisfies the campaign logictree. Accordingly, the GPU thread may set a value in an array indicatingthat the campaign logic tree is a “match” for the user profile. If,however, the campaign logic tree was “coffee AND tea,” then the userprofile would not satisfy the campaign logic tree because the “AND”operator would not be evaluated as true. The GPU thread may then set avalue in an array indicating that the campaign logic tree was not a“match” for the user profile.

VII. Tracking the Evaluation to Determine “Responsible Tags” DuringCampaign Evaluation

In certain embodiments, for each campaign logic tree that is listed as amatch, the GPU may track the evaluation to determine which operandvalues caused (e.g., were relevant to) determining the match. Continuingwith the illustrative example above, in which the campaign logic tree is“coffee OR tea” and the user profile includes an interest tagrepresenting “coffee,” the GPU thread may determine that the operator(e.g., “OR”) of the campaign logic tree has been satisfied, and thus,the campaign logic tree is determined to match the user profile. The GPUthread may also monitor which interest tag caused the user profile tosatisfy the campaign logic tree. For example, after the GPU threadevaluates a campaign logic tree by iteratively processing the operandvalues and operators of the campaign logic tree, the GPU also trackswhether the branch of the campaign logic tree is relevant or not.Additionally, if a campaign logic tree evaluates to “false” (e.g., noparent or root operators in the entire logic tree were satisfied as“true”), then none of the branches of the campaign logic tree would berelevant because no user interest caused the logic tree to be satisfiedas “true.” As another non-limiting example, a branch four levels deepwithin a campaign logic tree may appear to be relevant (e.g., may appearto cause the logic tree to be satisfied as true), but may ultimately bedetermined as not being irrelevant because a parent operator one or morelevels higher may be evaluated to false.

As another illustrative example, a campaign logic tree includes (OR cars(AND drinks food)) and a user profile includes the interest tag of“likes cars” and another interest tag of “likes drinks, but not food.”The GPU thread may track the evaluation for “responsible tags” byidentifying that only the “cars” branch is responsible for satisfyingthe logic as “true.” If the interest tag is responsible for satisfyingthe logic as “true,” the thread can mark the interest tag as a“responsible tag” by setting a bit in yet another array and store theinteger representing that interest (e.g., the integer representing“cars” in the example above) in an array of “responsible interests” forthat campaign logic tree.

As yet another illustrative example, before compiling the interest tagsresponsible for or contributing to satisfying each campaign logic treeas “true,” the GPU thread may first determine which interest tagscontributed to the campaign logic tree being evaluated as “true.” Aresponsible tag as used herein may refer to an interest tag thatcontributed to a campaign logic tree being evaluated as “true.” Forexample, if a campaign logic tree includes “coffee OR tea,” and a userprofile only includes an interest tag representing “tea,” then the GPUthread would return the interest tag of “tea” as a “responsible tag”that contributed to satisfying the campaign logic tree or contributed tothe evaluation of the logic tree as “true.”

VIII. Storing the “Responsible Tags” in the GPU-Optimized Data Structure

In certain embodiments, the matching protocol may allocate one or moretiers of storage space on the GPU for storing “responsible tags.” Theone or more tiers of the storage space may be specialized for storingdata processed by the GPU (e.g., the data stored being the “responsibletags”). While the matching protocol may allocate any number of storagespaces, in some implementations, the matching protocol allocates threestorage spaces: a primary storage space, a secondary storage space, anda ternary storage space. When the GPU identifies an interest tag thatcontributed to a match, the GPU attempts to first store that“responsible tag” in the primary storage space. If the primary storagespace is full, then the GPU attempts to store the “responsible tag” inthe secondary storage space, and so on. Each storage space may beprogressively shared by more GPU threads and may use one or more atomicoperations, which can affect a larger number of other GPU threads. Insome implementations, the primary storage space may be unique to eachGPU block and thread combination. Values can be saved in the primarystorage space without use of an atomic operation. The secondary spacemay include slots that are unique to each GPU block, but that are sharedacross all GPU threads. Values can be saved in the secondary storagespace using an atomic operation to allocate a slot to a thread. Theternary storage space may include one or more slots that are sharedacross the GPU blocks and threads. Values can be saved in the ternarystorage space using an atomic operation to allocate a slot to a blockand thread. The resulting data structure is a linked list for eachuser's campaign logic tree match.

IX. Generating a CPU-Readable Data Structure That Can Store the“Responsible Tags” in a CPU-Readable Manner

In certain embodiments, for each campaign logic tree associated with alinked list of “responsible tags” in the one or more tiers of theGPU-specialized storage spaces, the “responsible tags” may be copied toa new data structure that can be read by a CPU. The CPU may not beconfigured to efficiently read data from the one or more tiers of theGPU-specialized storage spaces described above, and thus, a new datastructure may be generated to store the “responsible tags” from theGPU-specialized storage spaces in a manner that is readable by a CPU.The new data structure may be configured into multiple segments thateach store 128 “responsible tags.” Each segment can store the“responsible tags” for multiple campaigns, unless the segment would“overflow,” in which case the “responsible tags” for the campaigncausing the overflow would be stored in the next segment. The“responsible tags” may be stored in the new data structure in a denseformat that permits a wide variety in the number of “responsible tags”that can be stored in a segment per campaign match. A given segment canstore up to a maximum number of “responsible tags” (e.g., a maximumnumber of 128). If, for example, only a few “responsible tags”contributed to a campaign logic tree evaluating as “true,” then thesegment available slots of the new data structure can be filled by the“responsible tags” of the next campaign logic tree, provided by that thesegment does not “overflow” by storing the “responsible tags” of thenext campaign logic tree. An overflow may be caused if the number of“responsible tags” of the next campaign logic tree plus the “responsibletags” already stored in a segment would exceed the maximum number of“responsible tags” that can be stored in the segment. If a potentialoverflow of a segment is detected, then the “responsible tags” of thenext campaign logic tree can be stored in the next available segment.

BRIEF DESCRIPTION OF THE DRAWINGS

The specification makes reference to the following appended figures, inwhich use of like reference numerals in different figures is intended toillustrate like or analogous components.

FIG. 1 is a block diagram illustrating an example of a networkenvironment, in which a cloud-based application evaluates one or morecharacteristics of a user profile to determine whether or not to offloadthe user profile to a GPU for processing, according to some aspects ofthe present disclosure.

FIG. 2 is a flowchart illustrating an example of a process foroffloading user profiles to a GPU for processing, according to someaspects.

FIG. 3 is a flowchart illustrating an example of a process forevaluating a set of user profiles against campaign logic trees,according to some aspects of the present disclosure.

FIG. 4 is a flowchart illustrating an example of a process fordetermining interest tags that are responsible for causing a campaignlogic tree to be satisfied, according to some aspects of the presentdisclosure.

FIG. 5 is a simplified diagram illustrating a distributed system forimplementing one of the embodiments.

FIG. 6 is a simplified block diagram illustrating one or more componentsof a system environment.

FIG. 7 illustrates an exemplary computer system, in which variousembodiments of the present invention may be implemented.

DETAILED DESCRIPTION

FIG. 1 is a block diagram illustrating an example of a networkenvironment, in which a cloud-based application evaluates one or morecharacteristics of a user profile to determine whether or not to offloadthe user profile to a GPU for processing, according to some aspects.Network environment 100 may include cloud network 110. Could network 110may be a cloud-based network that includes any number of cloud services,such as cloud service 130 (e.g., Oracle Data Cloud). Cloud service 130may be a cloud-based application that enables users to performfunctionality, for example, on user profiles stored in user profiledatabase 150. As a non-limiting example, cloud services 130 may includebusiness intelligence analytics, user profile querying, web analytics,and other suitable web analytics functionality.

Cloud service 130 may be configured to execute one or more functionsusing cloud-based server 140. Cloud-based server 140 may be a serverthat includes one or more local CPUs supporting the functionality ofcloud service 130. In some implementations, cloud server 130 may beconfigured to offload the processing of certain computation tasks to GPU160 for efficient parallel processing, instead of processing thecomputational task using cloud-based server 140. For example, cloudservice 130 may be configured to provide the functionality of evaluatingcampaign logic trees to determine which user profiles satisfy thecampaign logic trees. In this example, cloud-based server 140 may beconfigured to use the local CPU to execute the functionality ofcomparing user profiles to campaign logic trees.

In some implementations, cloud-based service 130 may evaluate one ormore characteristics of a user profile stored in user profile database150 to determine whether or not to offload the processing of the userprofile to GPU 160. Non-limiting examples of the characteristics mayinclude a number of interest tags included in the user profile, a userlocation indicated by information included in the user profile, or anysuitable information included in the user profile or associated with theuser profile. If the user profile satisfies an offloading condition(e.g., the number of interest tags included in the use profile exceeds athreshold number, such as 66), then the cloud service may offload thecomputational tasks involved with processing the user profile to GPU160. The GPU 160 may perform a matching protocol as described herein(e.g., in the “Summary” above) and other steps also described above toprocess the user profile. Further, the cloud service 130 may pre-processthe user profiles and the campaign logic trees database 170 also storedwithin cloud network 110 so as to store the user profiles and campaignlogic trees in data structures specialized for GPU processing. If theuser profile does not satisfy the offloading condition, then thecloud-based server 140 may perform one or more computational tasksinvolved with processing the user profile.

FIG. 2 is a flowchart illustrating an example process. Process 200 maybe performed, for example, by any of the components described herein,for example, cloud-based application 120, cloud service 130, cloud-basedserver 140, and/or GPU 160. Further, process 200 may be performed toenhance the computational efficiency of performing functionalitysupported by the cloud-based application 120.

Process 200 begins at block 210 where the cloud-based application 120 orthe cloud service 130 evaluates one or more characteristics of a userprofile to determine whether or not to offload the processing tasksassociated with that user profile to a GPU. Non-limiting examples of thecharacteristics may include a number of interest tags included in theuser profile, a user location indicated by information included in theuser profile, or any suitable information included in the user profileor associated with the user profile.

At block 220, the cloud-based application 120, for example, maydetermine whether or not an offloading condition has been satisfied. Theoffloading condition may be a threshold associated with a characteristicof the user profile. For example, the offloading condition may be athreshold number of interest tags included in the user profile. If thethreshold number of interest tags is exceeded, for example, then atblock 240, the cloud-based application may offload processing of theuser profile to the GPU. If the offloading condition is not satisfied,then at block 230, the cloud-based application 120 may execute thefunction for determining matches between campaigns and the user profileusing the local CPUs of the cloud-based server 140. At block 240, whenthe cloud-based application has determined to offload the processing ofthe user profile to the GPU, the GPU may perform a matching protocol(and other related tasks) to process the user profile.

FIG. 3 is a flowchart illustrating an example of a process forevaluating a set of user profiles against campaign logic trees,according to some aspects of the present disclosure. Process 300 may beperformed, for example, by any of the components described herein, forexample, cloud-based application 120, cloud service 130, cloud-basedserver 140, and/or GPU 160. Further, process 300 may be performed toevaluate interest tags of user profiles against a campaign logic tree toidentify which user profiles satisfy the logic of the campaign logictree in a computationally efficient manner.

Process 300 begins at block 310 where cloud-based application 120accesses or retrieves a set of user profiles. Each user profile caninclude any number of interest tags. The interest tags included in auser profile are determined based on interactions between the user and awebsite or a native application.

At block 320, cloud-based application 120 can pre-process the userprofiles for consuming by a GPU. Pre-processing the user profiles mayinclude generating a specialized data structure for storing the interesttags of the set of user profiles. The specialized data structure can beconfigured so that the threads of a warp of the GPU can access memoryadjacent to the one user profile's interest tag. Thus, thepre-processing can include organizing the interest tags included in eachuser profile in an interleaved manner to facilitate the coalescing ofread and write requests from the GPU processing cores.

Within block 320, cloud-based application 120 may also perform blocks322, 324, and 326. At block 322, cloud-based application 120 can segmentthe set of user profiles into one or more subsets of user profiles. Forexample, the set of user profiles (e.g., a batch of 256 user profiles)are split up into subsets, such that each subset is of a size thatmatches the warp size (e.g., 32 user profiles in a subset of userprofiles). At block 324, cloud-based application 120 can create at leasttwo arrays for storing interest tags of user profiles in an arrangementthat is consumable by a warp of the GPU. In some implementations,pre-processing the user profiles can include creating a first array,which is an array of all unique interest tags associated with the set ofuser profiles. The unique interest tags can be stored in a sorted order.For example, if there are N unique interest tags across the set of userprofiles, then the first array includes [interest_id_1 interest_id_2interest_id_3 . . . interest_id_N]. Additionally, pre-processing theuser profiles can include creating a second array of interest tags thatare interleaved over the set of user profiles. For example, if there are32 user profiles in a subset of user profiles, then the second array maybe a 2D array that includes [interest_id_1_applies_to_consumer_1interest_id_1_ applies_to_consumer_2 . . .interest_id_1_applies_to_consumer_32 interest_id_2_applies_to_consumer_1interest_id_2_ applies_to_consumer_2 . . .interest_id_2_applies_to_consumer_32 . . .interest_id_N_applies_to_consumer_1 interest_id_N_applies_to_consumer_2. . . interest_id_N_applies_to_consumer_32].

Organizing the specialized data structure storing interest tags of userprofiles enables each GPU thread in the warp to iterate in lock-stepthrough the interest tags stored in the user interest array (e.g., afirst interest tag stored in the array through the last interest tagstored in the array). For each interest tag stored in the array, the GPUthread may determine whether the user profile (that corresponds to theGPU thread) includes that interest tag. If the user profile includesthat interest tag, then the GPU thread may set a bit corresponding tothat particular interest tag's slot in an array corresponding to theuser profile (e.g., the user interests array) as true. Each GPU threadmay access memory adjacent to the memory associated with the other userprofiles, and as such, the read requests from the GPU are coalesced(e.g., if the GPU reads in one user's interest tag bit, the GPU willlikely already be loading the other interest tags of the other userprofiles in that GPU warp for that particular interest tag). At block326, cloud-based application 120 can generate the specialized datastructure using the first array and the second array. The specializeddata structure can then store the various interest tags of the set ofuser profiles.

At block 330, the warp of the GPU can read from the specialized datastructure to evaluate whether the interest tags of user profiles satisfythe logic of campaign logic trees. In some implementations, theevaluation at block 330 may be performed using a matching protocol,which can be executed by warp of the GPU. The matching protocol isdescribed in greater detail with respect to FIG. 4.

FIG. 4 is a flowchart illustrating an example of a process fordetermining interest tags that are responsible for causing a campaignlogic tree to be satisfied, according to some aspects of the presentdisclosure. Process 400 may be performed, for example, by any of thecomponents described herein, for example, cloud-based application 120,cloud service 130, cloud-based server 140, and/or GPU 160. Further,process 400 may be performed to identify interest tags that areresponsible (e.g., “responsible tags”) for causing a campaign logic treeto be satisfied as “true,” and to store the “responsible tags” in aGPU-specialized data structure and a CPU-oriented data structure.

Process 400 begins at block 410 where the GPU accesses a set of campaignlogic trees. For example, the GPU can retrieve the set of campaign logictrees from a GPU-specialized data structure configured to storeflattened versions of the campaign logic trees in a sequential order. Atblock 420, the GPU can also access a set of user profiles to evaluateagainst the campaign logic trees. For example, the GPU can retrieve theset of user profiles from another GPU-specialized data structure that isconfigured to store the interest tags of various user profiles.

At block 430, the GPU evaluates each campaign logic tree using theinterest tags of a user profile to determine which interest tags areresponsible for causing the user profile to match or satisfy thecampaign logic tree as true. Block 430 can include blocks 432, 434, 436,and 438. At block 430, the set of threads of the GPU can be segmentedinto subsets of threads. For example, a subset of threads can correspondto a warp of the GPU, and thus, can include 32 threads. At block 434,the GPU can retrieve the campaign logic trees from the GPU-specializeddata structure that is configured to store flattened versions of thecampaign logic trees. For example, the GPU can load data from thecampaign operand values array and the campaign operator array (asdescribed above in the present disclosure) for each campaign logic tree.At block 436, the GPU can iteratively process the retrieved campaignlogic trees using the interest tags of user profiles accessed at block420. Lastly, at block 438, the GPU can determine which interest tags ofa user profile cause the logic of campaign logic trees to be satisfiedas “true.”

Blocks 436 and 438 may represent an example of a matching protocol thatis performed by the GPU to evaluate the user profiles against thecampaign logic trees to determine which user profiles match the campaignlogic trees. As an illustrative example, the matching protocol mayinclude the steps described below. Each thread of the warp can retrievean interest tag from the second array (e.g., the user interest array asdescribe with respect to FIG. 3). For every interest tag, the thread canidentify the operand values of various campaign logic trees thatreference the interest tag (e.g., using the interest map array, whichmaps interest tags to offset operand values identifying the position ofoperands relating to each campaign logic tree). The thread can mark thatoperand as true by setting a bit in another array to “1” for example.Additionally, the thread can mark the campaign logic tree thatreferences that operand as a possible match by setting a bit to “1” inyet another array. For every campaign logic tree marked as a possiblematch, the thread can evaluate the logic of the campaign logic tree todetermine if the logic has been satisfied as “true.” If the logic hasbeen satisfied, then the thread sets another bit in another array toindicate that the campaign logic tree has been satisfied.

For every campaign logic tree that has been satisfied as “true,” thethread can determine, for every operand in the campaign logic tree,whether that operand was responsible for satisfying the logic as “true.”For example, in a campaign logic tree with (OR cars (AND drinks food))and a user profile that includes the interest tag of “likes cars” andanother interest tag of “likes drinks, but not food,” then only the“cars” branch of the campaign logic tree would be responsible forsatisfying the logic as “true.” Thus, the interest tag of “likes cars”would be responsible for satisfying the logic as “true,” If the operandis responsible for satisfying the logic as “true,” the thread can markthe operand as a “responsible tag” by setting a bit in yet another arrayand store the integer representing that interest (e.g., the integerrepresenting “cars” in the example above) in an array of “responsibleinterests” for that campaign logic tree. Further, for example, if anoperand targets “cars,” and a user profile is flagged for “cars”,“pickup trucks”, and “sports cars”, the thread can record all threeoperands as a “responsible interest” for that operand. Lastly, for everycampaign logic tree that was evaluated to a “true” result, and for everyoperand value of the campaign logic tree, the thread can append thearray of interests for that operand to a list of interests responsiblefor causing the campaign to evaluate as “true.”

FIG. 5 depicts a simplified diagram of a distributed system 500 forimplementing one of the embodiments. In the illustrated embodiment,distributed system 500 includes one or more client computing devices502, 504, 506, and 508, which are configured to execute and operate aclient application such as a web browser, proprietary client (e.g.,Oracle Forms), or the like over one or more network(s) 510. Server 512may be communicatively coupled with remote client computing devices 502,504, 506, and 508 via network 510.

In various embodiments, server 512 may be adapted to run one or moreservices or software applications provided by one or more of thecomponents of the system. In some embodiments, these services may beoffered as web-based or cloud services or under a Software as a Service(SaaS) model to the users of client computing devices 502, 504, 506,and/or 508. Users operating client computing devices 502, 504, 506,and/or 508 may in turn utilize one or more client applications tointeract with server 512 to utilize the services provided by thesecomponents.

In the configuration depicted in the figure, the software components518, 520 and 522 of system 500 are shown as being implemented on server512. In other embodiments, one or more of the components of system 500and/or the services provided by these components may also be implementedby one or more of the client computing devices 502, 504, 506, and/or508. Users operating the client computing devices may then utilize oneor more client applications to use the services provided by thesecomponents. These components may be implemented in hardware, firmware,software, or combinations thereof. It should be appreciated that variousdifferent system configurations are possible, which may be differentfrom distributed system 500. The embodiment shown in the figure is thusone example of a distributed system for implementing an embodimentsystem and is not intended to be limiting.

Client computing devices 502, 504, 506, and/or 508 may be portablehandheld devices (e.g., an iPhone®, cellular telephone, an iPad®,computing tablet, a personal digital assistant (PDA)) or wearabledevices (e.g., a Google Glass® head mounted display), running softwaresuch as Microsoft Windows Mobile®, and/or a variety of mobile operatingsystems such as iOS, Windows Phone, Android, BlackBerry 10, Palm OS, andthe like, and being Internet, e-mail, short message service (SMS),Blackberry®, or other communication protocol enabled. The clientcomputing devices can be general purpose personal computers including,by way of example, personal computers and/or laptop computers runningvarious versions of Microsoft Windows®, Apple Macintosh®, and/or Linuxoperating systems. The client computing devices can be workstationcomputers running any of a variety of commercially-available UNIX® orUNIX-like operating systems, including without limitation the variety ofGNU/Linux operating systems, such as for example, Google Chrome OS.Alternatively, or in addition, client computing devices 502, 504, 506,and 508 may be any other electronic device, such as a thin-clientcomputer, an Internet-enabled gaming system (e.g., a Microsoft Xboxgaming console with or without a Kinect® gesture input device), and/or apersonal messaging device, capable of communicating over network(s) 510.

Although exemplary distributed system 500 is shown with four clientcomputing devices, any number of client computing devices may besupported. Other devices, such as devices with sensors, etc., mayinteract with server 512.

Network(s) 510 in distributed system 500 may be any type of networkfamiliar to those skilled in the art that can support datacommunications using any of a variety of commercially-availableprotocols, including without limitation TCP/IP (transmission controlprotocol/Internet protocol), SNA (systems network architecture), IPX(Internet packet exchange), AppleTalk, and the like. Merely by way ofexample, network(s) 510 can be a local area network (LAN), such as onebased on Ethernet, Token-Ring and/or the like. Network(s) 510 can be awide-area network and the Internet. It can include a virtual network,including without limitation a virtual private network (VPN), anintranet, an extranet, a public switched telephone network (PSTN), aninfra-red network, a wireless network (e.g., a network operating underany of the Institute of Electrical and Electronics (IEEE) 802.11 suiteof protocols, Bluetooth®, and/or any other wireless protocol); and/orany combination of these and/or other networks.

Server 512 may be composed of one or more general purpose computers,specialized server computers (including, by way of example, PC (personalcomputer) servers, UNIX® servers, mid-range servers, mainframecomputers, rack-mounted servers, etc.), server farms, server clusters,or any other appropriate arrangement and/or combination. In variousembodiments, server 512 may be adapted to run one or more services orsoftware applications described in the foregoing disclosure. Forexample, server 512 may correspond to a server for performing processingdescribed above according to an embodiment of the present disclosure.

Server 512 may run an operating system including any of those discussedabove, as well as any commercially available server operating system.Server 512 may also run any of a variety of additional serverapplications and/or mid-tier applications, including HTTP (hypertexttransport protocol) servers, FTP (file transfer protocol) servers, CGI(common gateway interface) servers, JAVA® servers, database servers, andthe like. Exemplary database servers include without limitation thosecommercially available from Oracle, Microsoft, Sybase, IBM(International Business Machines), and the like.

In some implementations, server 512 may include one or more applicationsto analyze and consolidate data feeds and/or event updates received fromusers of client computing devices 502, 504, 506, and 508. As an example,data feeds and/or event updates may include, but are not limited to,Twitter® feeds, Facebook® updates or real-time updates received from oneor more third party information sources and continuous data streams,which may include real-time events related to sensor data applications,financial tickers, network performance measuring tools (e.g., networkmonitoring and traffic management applications), clickstream analysistools, automobile traffic monitoring, and the like. Server 512 may alsoinclude one or more applications to display the data feeds and/orreal-time events via one or more display devices of client computingdevices 502, 504, 506, and 508.

Distributed system 500 may also include one or more databases 514 and516. Databases 514 and 516 may reside in a variety of locations. By wayof example, one or more of databases 514 and 516 may reside on anon-transitory storage medium local to (and/or resident in) server 512.Alternatively, databases 514 and 516 may be remote from server 512 andin communication with server 512 via a network-based or dedicatedconnection. In one set of embodiments, databases 514 and 516 may residein a storage-area network (SAN). Similarly, any necessary files forperforming the functions attributed to server 512 may be stored locallyon server 512 and/or remotely, as appropriate. In one set ofembodiments, databases 514 and 516 may include relational databases,such as databases provided by Oracle, that are adapted to store, update,and retrieve data in response to SQL-formatted commands.

FIG. 6 is a simplified block diagram of one or more components of asystem environment 600 by which services provided by one or morecomponents of an embodiment system may be offered as cloud services, inaccordance with an embodiment of the present disclosure. In theillustrated embodiment, system environment 600 includes one or moreclient computing devices 604, 606, and 608 that may be used by users tointeract with a cloud infrastructure system 602 that provides cloudservices. The client computing devices may be configured to operate aclient application such as a web browser, a proprietary clientapplication (e.g., Oracle Forms), or some other application, which maybe used by a user of the client computing device to interact with cloudinfrastructure system 602 to use services provided by cloudinfrastructure system 602.

It should be appreciated that cloud infrastructure system 602 depictedin the figure may have other components than those depicted. Further,the embodiment shown in the figure is only one example of a cloudinfrastructure system that may incorporate an embodiment of theinvention. In some other embodiments, cloud infrastructure system 602may have more or fewer components than shown in the figure, may combinetwo or more components, or may have a different configuration orarrangement of components.

Client computing devices 604, 606, and 608 may be devices similar tothose described above for 502, 504, 506, and 508.

Although exemplary system environment 600 is shown with three clientcomputing devices, any number of client computing devices may besupported. Other devices such as devices with sensors, etc. may interactwith cloud infrastructure system 602.

Network(s) 610 may facilitate communications and exchange of databetween clients 604, 606, and 608 and cloud infrastructure system 602.Each network may be any type of network familiar to those skilled in theart that can support data communications using any of a variety ofcommercially-available protocols, including those described above fornetwork(s) 510.

Cloud infrastructure system 602 may comprise one or more computersand/or servers that may include those described above for server 512.

In certain embodiments, services provided by the cloud infrastructuresystem may include a host of services that are made available to usersof the cloud infrastructure system on demand, such as online datastorage and backup solutions, Web-based e-mail services, hosted officesuites and document collaboration services, database processing, managedtechnical support services, and the like. Services provided by the cloudinfrastructure system can dynamically scale to meet the needs of itsusers. A specific instantiation of a service provided by cloudinfrastructure system is referred to herein as a “service instance.” Ingeneral, any service made available to a user via a communicationnetwork, such as the Internet, from a cloud service provider's system isreferred to as a “cloud service.” Typically, in a public cloudenvironment, servers and systems that make up the cloud serviceprovider's system are different from the customer's own on-premisesservers and systems. For example, a cloud service provider's system mayhost an application, and a user may, via a communication network such asthe Internet, on demand, order and use the application.

In some examples, a service in a computer network cloud infrastructuremay include protected computer network access to storage, a hosteddatabase, a hosted web server, a software application, or other serviceprovided by a cloud vendor to a user, or as otherwise known in the art.For example, a service can include password-protected access to remotestorage on the cloud through the Internet. As another example, a servicecan include a web service-based hosted relational database and ascript-language middleware engine for private use by a networkeddeveloper. As another example, a service can include access to an emailsoftware application hosted on a cloud vendor's web site.

In certain embodiments, cloud infrastructure system 602 may include asuite of applications, middleware, and database service offerings thatare delivered to a customer in a self-service, subscription-based,elastically scalable, reliable, highly available, and secure manner. Anexample of such a cloud infrastructure system is the Oracle Public Cloudprovided by the present assignee.

In various embodiments, cloud infrastructure system 602 may be adaptedto automatically provision, manage and track a customer's subscriptionto services offered by cloud infrastructure system 602. Cloudinfrastructure system 602 may provide the cloud services via differentdeployment models. For example, services may be provided under a publiccloud model in which cloud infrastructure system 602 is owned by anorganization selling cloud services (e.g., owned by Oracle) and theservices are made available to the general public or different industryenterprises. As another example, services may be provided under aprivate cloud model in which cloud infrastructure system 602 is operatedsolely for a single organization and may provide services for one ormore entities within the organization. The cloud services may also beprovided under a community cloud model in which cloud infrastructuresystem 602 and the services provided by cloud infrastructure system 602are shared by several organizations in a related community. The cloudservices may also be provided under a hybrid cloud model, which is acombination of two or more different models.

In some embodiments, the services provided by cloud infrastructuresystem 802 may include one or more services provided under Software as aService (SaaS) category, Platform as a Service (PaaS) category,Infrastructure as a Service (IaaS) category, or other categories ofservices including hybrid services. A customer, via a subscriptionorder, may order one or more services provided by cloud infrastructuresystem 602. Cloud infrastructure system 602 then performs processing toprovide the services in the customer's subscription order.

In some embodiments, the services provided by cloud infrastructuresystem 602 may include, without limitation, application services,platform services and infrastructure services. In some examples,application services may be provided by the cloud infrastructure systemvia a SaaS platform. The SaaS platform may be configured to providecloud services that fall under the SaaS category. For example, the SaaSplatform may provide capabilities to build and deliver a suite ofon-demand applications on an integrated development and deploymentplatform. The SaaS platform may manage and control the underlyingsoftware and infrastructure for providing the SaaS services. Byutilizing the services provided by the SaaS platform, customers canutilize applications executing on the cloud infrastructure system.Customers can acquire the application services without the need forcustomers to purchase separate licenses and support. Various differentSaaS services may be provided. Examples include, without limitation,services that provide solutions for sales performance management,enterprise integration, and flexibility for large organizations.

In some embodiments, platform services may be provided by the cloudinfrastructure system via a PaaS platform. The PaaS platform may beconfigured to provide cloud services that fall under the PaaS category.Examples of platform services may include without limitation servicesthat enable organizations (such as Oracle) to consolidate existingapplications on a shared, common architecture, as well as the ability tobuild new applications that leverage the shared services provided by theplatform. The PaaS platform may manage and control the underlyingsoftware and infrastructure for providing the PaaS services. Customerscan acquire the PaaS services provided by the cloud infrastructuresystem without the need for customers to purchase separate licenses andsupport. Examples of platform services include, without limitation,Oracle Java Cloud Service (JCS), Oracle Database Cloud Service (DBCS),and others.

By utilizing the services provided by the PaaS platform, customers canemploy programming languages and tools supported by the cloudinfrastructure system and also control the deployed services. In someembodiments, platform services provided by the cloud infrastructuresystem may include database cloud services, middleware cloud services(e.g., Oracle Fusion Middleware services), and Java cloud services. Inone embodiment, database cloud services may support shared servicedeployment models that enable organizations to pool database resourcesand offer customers a Database as a Service in the form of a databasecloud. Middleware cloud services may provide a platform for customers todevelop and deploy various cloud applications, and Java cloud servicesmay provide a platform for customers to deploy Java applications, in thecloud infrastructure system.

Various different infrastructure services may be provided by an IaaSplatform in the cloud infrastructure system. The infrastructure servicesfacilitate the management and control of the underlying computingresources, such as storage, networks, and other fundamental computingresources for customers utilizing services provided by the SaaS platformand the PaaS platform.

In certain embodiments, cloud infrastructure system 602 may also includeinfrastructure resources 630 for providing the resources used to providevarious services to customers of the cloud infrastructure system. In oneembodiment, infrastructure resources 630 may include pre-integrated andoptimized combinations of hardware, such as servers, storage, andnetworking resources to execute the services provided by the PaaSplatform and the SaaS platform.

In some embodiments, resources in cloud infrastructure system 602 may beshared by multiple users and dynamically re-allocated per demand.Additionally, resources may be allocated to users in different timezones. For example, cloud infrastructure system 630 may enable a firstset of users in a first time zone to utilize resources of the cloudinfrastructure system for a specified number of hours and then enablethe re-allocation of the same resources to another set of users locatedin a different time zone, thereby maximizing the utilization ofresources.

In certain embodiments, a number of internal shared services 632 may beprovided that are shared by different components or modules of cloudinfrastructure system 602 and by the services provided by cloudinfrastructure system 602. These internal shared services may include,without limitation, a security and identity service, an integrationservice, an enterprise repository service, an enterprise managerservice, a virus scanning and white list service, a high availability,backup and recovery service, service for enabling cloud support, anemail service, a notification service, a file transfer service, and thelike.

In certain embodiments, cloud infrastructure system 602 may providecomprehensive management of cloud services (e.g., SaaS, PaaS, and IaaSservices) in the cloud infrastructure system. In one embodiment, cloudmanagement functionality may include capabilities for provisioning,managing and tracking a customer's subscription received by cloudinfrastructure system 602, and the like.

In one embodiment, as depicted in the figure, cloud managementfunctionality may be provided by one or more modules, such as an ordermanagement module 620, an order orchestration module 622, an orderprovisioning module 624, an order management and monitoring module 626,and an identity management module 628. These modules may include or beprovided using one or more computers and/or servers, which may begeneral purpose computers, specialized server computers, server farms,server clusters, or any other appropriate arrangement and/orcombination.

In exemplary operation 634, a customer using a client device, such asclient device 604, 606 or 608, may interact with cloud infrastructuresystem 602 by requesting one or more services provided by cloudinfrastructure system 602 and placing an order for a subscription forone or more services offered by cloud infrastructure system 602. Incertain embodiments, the customer may access a cloud User Interface(UI), cloud UI 612, cloud UI 614 and/or cloud UI 616 and place asubscription order via these UIs. The order information received bycloud infrastructure system 602 in response to the customer placing anorder may include information identifying the customer and one or moreservices offered by the cloud infrastructure system 602 that thecustomer intends to subscribe to.

After an order has been placed by the customer, the order information isreceived via the cloud UIs, 612, 614 and/or 616.

At operation 636, the order is stored in order database 618. Orderdatabase 618 can be one of several databases operated by cloudinfrastructure system 618 and operated in conjunction with other systemelements.

At operation 638, the order information is forwarded to an ordermanagement module 620. In some instances, order management module 620may be configured to perform billing and accounting functions related tothe order, such as verifying the order, and upon verification, bookingthe order.

At operation 640, information regarding the order is communicated to anorder orchestration module 622. Order orchestration module 622 mayutilize the order information to orchestrate the provisioning ofservices and resources for the order placed by the customer. In someinstances, order orchestration module 622 may orchestrate theprovisioning of resources to support the subscribed services using theservices of order provisioning module 624.

In certain embodiments, order orchestration module 622 enables themanagement of processes associated with each order and applies logic todetermine whether an order should proceed to provisioning. At operation642, upon receiving an order for a new subscription, order orchestrationmodule 622 sends a request to order provisioning module 624 to allocateresources and configure those resources needed to fulfill thesubscription order. Order provisioning module 624 enables the allocationof resources for the services ordered by the customer. Orderprovisioning module 624 provides a level of abstraction between thecloud services provided by cloud infrastructure system 600 and thephysical implementation layer that is used to provision the resourcesfor providing the requested services. Order orchestration module 622 maythus be isolated from implementation details, such as whether or notservices and resources are actually provisioned on the fly orpre-provisioned and only allocated/assigned upon request.

At operation 644, once the services and resources are provisioned, anotification of the provided service may be sent to customers on clientdevices 604, 606 and/or 608 by order provisioning module 624 of cloudinfrastructure system 602.

At operation 646, the customer's subscription order may be managed andtracked by an order management and monitoring module 626. In someinstances, order management and monitoring module 626 may be configuredto collect usage statistics for the services in the subscription order,such as the amount of storage used, the amount data transferred, thenumber of users, and the amount of system up time and system down time.

In certain embodiments, cloud infrastructure system 600 may include anidentity management module 628. Identity management module 628 may beconfigured to provide identity services, such as access management andauthorization services in cloud infrastructure system 600. In someembodiments, identity management module 628 may control informationabout customers who wish to utilize the services provided by cloudinfrastructure system 602. Such information can include information thatauthenticates the identities of such customers and information thatdescribes which actions those customers are authorized to performrelative to various system resources (e.g., files, directories,applications, communication ports, memory segments, etc.) Identitymanagement module 628 may also include the management of descriptiveinformation about each customer and about how and by whom thatdescriptive information can be accessed and modified.

FIG. 7 illustrates an exemplary computer system 700, in which variousembodiments of the present invention may be implemented. The system 700may be used to implement any of the computer systems described above. Asshown in the figure, computer system 700 includes a processing unit 704that communicates with a number of peripheral subsystems via a bussubsystem 702. These peripheral subsystems may include a processingacceleration unit 706, an I/O subsystem 708, a storage subsystem 718 anda communications subsystem 724. Storage subsystem 718 includes tangiblecomputer-readable storage media 722 and a system memory 710.

Bus subsystem 702 provides a mechanism for letting the variouscomponents and subsystems of computer system 700 communicate with eachother as intended. Although bus subsystem 702 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 702 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Forexample, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 704, which can be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 700. One or more processorsmay be included in processing unit 704. These processors may includesingle core or multicore processors. In certain embodiments, processingunit 704 may be implemented as one or more independent processing units732 and/or 734 with single or multicore processors included in eachprocessing unit. In other embodiments, processing unit 704 may also beimplemented as a quad-core processing unit formed by integrating twodual-core processors into a single chip.

In various embodiments, processing unit 704 can execute a variety ofprograms in response to program code and can maintain multipleconcurrently executing programs or processes. At any given time, some orall of the program code to be executed can be resident in processor(s)704 and/or in storage subsystem 718. Through suitable programming,processor(s) 704 can provide various functionalities described above.Computer system 700 may additionally include a processing accelerationunit 706, which can include a digital signal processor (DSP), aspecial-purpose processor, and/or the like.

I/O subsystem 708 may include user interface input devices and userinterface output devices. User interface input devices may include akeyboard, pointing devices such as a mouse or trackball, a touchpad ortouch screen incorporated into a display, a scroll wheel, a click wheel,a dial, a button, a switch, a keypad, audio input devices with voicecommand recognition systems, microphones, and other types of inputdevices. User interface input devices may include, for example, motionsensing and/or gesture recognition devices such as the Microsoft Kinect®motion sensor that enables users to control and interact with an inputdevice, such as the Microsoft Xbox® 360 game controller, through anatural user interface using gestures and spoken commands. Userinterface input devices may also include eye gesture recognition devicessuch as the Google Glass® blink detector that detects eye activity(e.g., ‘blinking’ while taking pictures and/or making a menu selection)from users and transforms the eye gestures as input into an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Siri® navigator), through voicecommands.

User interface input devices may also include, without limitation, threedimensional (3D) mice, joysticks or pointing sticks, gamepads andgraphic tablets, and audio/visual devices such as speakers, digitalcameras, digital camcorders, portable media players, webcams, imagescanners, fingerprint scanners, barcode reader 3D scanners, 3D printers,laser rangefinders, and eye gaze tracking devices. Additionally, userinterface input devices may include, for example, medical imaging inputdevices such as computed tomography, magnetic resonance imaging,position emission tomography, medical ultrasonography devices. Userinterface input devices may also include, for example, audio inputdevices such as MIDI keyboards, digital musical instruments and thelike.

User interface output devices may include a display subsystem, indicatorlights, or non-visual displays such as audio output devices, etc. Thedisplay subsystem may be a cathode ray tube (CRT), a flat-panel device,such as that using a liquid crystal display (LCD) or plasma display, aprojection device, a touch screen, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system700 to a user or other computer. For example, user interface outputdevices may include, without limitation, a variety of display devicesthat visually convey text, graphics and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Computer system 700 may comprise a storage subsystem 718 that comprisessoftware elements, shown as being currently located within a systemmemory 710. System memory 710 may store program instructions that areloadable and executable on processing unit 704, as well as datagenerated during the execution of these programs.

Depending on the configuration and type of computer system 700, systemmemory 710 may be volatile (such as random access memory (RAM)) and/ornon-volatile (such as read-only memory (ROM), flash memory, etc.) TheRAM typically contains data and/or program modules that are immediatelyaccessible to and/or presently being operated and executed by processingunit 704. In some implementations, system memory 710 may includemultiple different types of memory, such as static random access memory(SRAM) or dynamic random access memory (DRAM). In some implementations,a basic input/output system (BIOS), containing the basic routines thathelp to transfer information between elements within computer system700, such as during start-up, may typically be stored in the ROM. By wayof example, and not limitation, system memory 710 also illustratesapplication programs 712, which may include client applications, Webbrowsers, mid-tier applications, relational database management systems(RDBMS), etc., program data 714, and an operating system 716. By way ofexample, operating system 716 may include various versions of MicrosoftWindows®, Apple Macintosh®, and/or Linux operating systems, a variety ofcommercially-available UNIX® or UNIX-like operating systems (includingwithout limitation the variety of GNU/Linux operating systems, theGoogle Chrome® OS, and the like) and/or mobile operating systems such asiOS, Windows® Phone, Android® OS, BlackBerry® 10 OS, and Palm® OSoperating systems.

Storage subsystem 718 may also provide a tangible computer-readablestorage medium for storing the basic programming and data constructsthat provide the functionality of some embodiments. Software (programs,code modules, instructions) that when executed by a processor providethe functionality described above may be stored in storage subsystem718. These software modules or instructions may be executed byprocessing unit 704. Storage subsystem 718 may also provide a repositoryfor storing data used in accordance with the present invention.

Storage subsystem 700 may also include a computer-readable storage mediareader 720 that can further be connected to computer-readable storagemedia 722. Together and, optionally, in combination with system memory710, computer-readable storage media 722 may comprehensively representremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containing, storing,transmitting, and retrieving computer-readable information.

Computer-readable storage media 722 containing code, or portions ofcode, can also include any appropriate media known or used in the art,including storage media and communication media, such as but not limitedto, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computing system 700.

By way of example, computer-readable storage media 722 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 722 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 722 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 700.

Communications subsystem 724 provides an interface to other computersystems and networks. Communications subsystem 724 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 700. For example, communications subsystem 924 mayenable computer system 700 to connect to one or more devices via theInternet. In some embodiments communications subsystem 724 can includeradio frequency (RF) transceiver components for accessing wireless voiceand/or data networks (e.g., using cellular telephone technology,advanced data network technology, such as 3G, 4G or EDGE (enhanced datarates for global evolution), WiFi (IEEE 1202.11 family standards, orother mobile communication technologies, or any combination thereof),global positioning system (GPS) receiver components, and/or othercomponents. In some embodiments communications subsystem 724 can providewired network connectivity (e.g., Ethernet) in addition to or instead ofa wireless interface.

In some embodiments, communications subsystem 724 may also receive inputcommunication in the form of structured and/or unstructured data feeds726, event streams 728, event updates 730, and the like on behalf of oneor more users who may use computer system 700.

By way of example, communications subsystem 724 may be configured toreceive data feeds 726 in real-time from users of social networks and/orother communication services such as Twitter® feeds, Facebook® updates,web feeds such as Rich Site Summary (RSS) feeds, and/or real-timeupdates from one or more third party information sources.

Additionally, communications subsystem 724 may also be configured toreceive data in the form of continuous data streams, which may includeevent streams 728 of real-time events and/or event updates 730, that maybe continuous or unbounded in nature with no explicit end. Examples ofapplications that generate continuous data may include, for example,sensor data applications, financial tickers, network performancemeasuring tools (e.g. network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 724 may also be configured to output thestructured and/or unstructured data feeds 726, event streams 728, eventupdates 730, and the like to one or more databases that may be incommunication with one or more streaming data source computers coupledto computer system 700.

Computer system 700 can be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a PC, a workstation, a mainframe, a kiosk, a server rack, orany other data processing system.

Due to the ever-changing nature of computers and networks, thedescription of computer system 700 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software (includingapplets), or a combination. Further, connection to other computingdevices, such as network input/output devices, may be employed. Based onthe disclosure and teachings provided herein, a person of ordinary skillin the art will appreciate other ways and/or methods to implement thevarious embodiments.

In the foregoing specification, aspects of the invention are describedwith reference to specific embodiments thereof, but those skilled in theart will recognize that the invention is not limited thereto. Variousfeatures and aspects of the above-described invention may be usedindividually or jointly. Further, embodiments can be utilized in anynumber of environments and applications beyond those described hereinwithout departing from the broader spirit and scope of thespecification. The specification and drawings are, accordingly, to beregarded as illustrative rather than restrictive.

1. A computer-implemented method, comprising: providing a computerprocessing unit (CPU) and a graphical processing unit (GPU) in acloud-based network; accessing one or more user profiles stored at auser profile database, each user profile of the one or more userprofiles including one or more interest tags, each interest tag of theone or more interest tags characterizing an interest of a user detectedfrom an interaction between a user device and a web server or anapplication server; accessing one or more campaign logic trees, eachcampaign logic tree of the one or more campaign logic trees includingone or more nodes representing logic for determining whether a userprofile satisfies the campaign logic tree, and each node of the one ormore nodes corresponding to an operand value or an operator; for eachuser profile of the one or more user profiles: evaluating one or morecharacteristics of the user profile using an offloading condition, theevaluation of the one or more characteristics generating a resultindicating whether or not the user profile satisfies the offloadingcondition; when the result indicates that the user profile does notsatisfy the offloading condition, using the CPU to perform a process fordetermining whether or not the user profile satisfies each campaignlogic tree of the one or more campaign logic trees; and when the resultindicates that the user profile satisfies the offloading condition,assigning to the GPU a task of performing the process for determiningwhether or not the user profile satisfies each campaign logic tree ofthe one or more campaign logic trees; and generating an output datastructure including at least one user profile of one or more userprofiles, wherein the at least one user profile satisfies at least onecampaign logic tree of the one or more campaign logic trees.
 2. Thecomputer-implemented method of claim 1, further comprising: transformingthe one or more campaign logic trees into a pre-processedGPU-specialized campaign data structure configured for processing by theGPU; and transforming the one or more user profiles into a pre-processedGPU-specialized user data structure configured for processing by theGPU.
 3. The computer-implemented method of claim 2, wherein transformingthe one or more campaign logic trees into the pre-processedGPU-specialized campaign data structure further comprises: transformingthe one or more campaign logic trees into a global campaign arrayincluding only one or more operand values and one or more operators ofeach campaign logic tree of the one or more campaign logic trees; andtransforming the global campaign array into: an operand value arrayincluding the one or more operand values of each campaign logic tree ofthe one or more campaign logic trees; and an operator array includingthe one or more operators for each campaign logic tree of the one ormore campaign logic trees.
 4. The computer-implemented method of claim3, further comprising: generating an interest map array, which maps eachinterest tag of a plurality of interest tags included in the one or moreuser profiles to an operand value of the operand value array.
 5. Thecomputer-implemented method of claim 2, wherein transforming the one ormore user profiles into the pre-processed GPU-specialized user datastructure further comprises: for each user profile of the one or moreuser profiles: storing a variable value for each interest tag in anarray, the variable value indicating whether or not the interest tag isincluded in the user profile, and the variable value for each userprofile of the one or more user profiles being stored in the array in aninterleaved manner.
 6. The computer-implemented method of claim 1,wherein the offloading condition is satisfied when a user profile of theone or more user profiles includes a threshold number of interest tags.7. The computer-implemented method of claim 1, further comprising:segmenting a set of threads of the GPU into one or more warps of theGPU; segmenting a set of user profiles into one or more subsets of theset of user profiles; and assigning a subset of the set of user profilesto a warp of the one or more warps of the GPU, wherein when the GPUperforms the process, the GPU simultaneously evaluates each user profileof the subset of user profiles assigned to the warp against a campaignlogic tree of the one or more campaign logic tree.
 8. A system,comprising: one or more processors including a computer processing unit(CPU) and a graphical processing unit (GPU) in a cloud-based network;and a non-transitory computer-readable storage medium containinginstructions which, when executed on the one or more processors, causethe one or more processors to perform operations including: accessingone or more user profiles stored at a user profile database, each userprofile of the one or more user profiles including one or more interesttags, each interest tag of the one or more interest tags characterizingan interest of a user detected from an interaction between a user deviceand a web server or an application server; accessing one or morecampaign logic trees, each campaign logic tree of the one or morecampaign logic trees including one or more nodes representing logic fordetermining whether a user profile satisfies the campaign logic tree,and each node of the one or more nodes corresponding to an operand valueor an operator; for each user profile of the one or more user profiles:evaluating one or more characteristics of the user profile using anoffloading condition, the evaluation of the one or more characteristicsgenerating a result indicating whether or not the user profile satisfiesthe offloading condition; when the result indicates that the userprofile does not satisfy the offloading condition, using the CPU toperform a process for determining whether or not the user profilesatisfies each campaign logic tree of the one or more campaign logictrees; and when the result indicates that the user profile satisfies theoffloading condition, assigning to the GPU a task of performing theprocess for determining whether or not the user profile satisfies eachcampaign logic tree of the one or more campaign logic trees; andgenerating an output data structure including at least one user profileof one or more user profiles, wherein the at least one user profilesatisfies at least one campaign logic tree of the one or more campaignlogic trees.
 9. The system of claim 8, wherein the operations furthercomprise: transforming the one or more campaign logic trees into apre-processed GPU-specialized campaign data structure configured forprocessing by the GPU; and transforming the one or more user profilesinto a pre-processed GPU-specialized user data structure configured forprocessing by the GPU.
 10. The system of claim 9, wherein transformingthe one or more campaign logic trees into the pre-processedGPU-specialized campaign data structure further comprises: transformingthe one or more campaign logic trees into a global campaign arrayincluding only one or more operand values and one or more operators ofeach campaign logic tree of the one or more campaign logic trees; andtransforming the global campaign array into: an operand value arrayincluding the one or more operand values of each campaign logic tree ofthe one or more campaign logic trees; and an operator array includingthe one or more operators for each campaign logic tree of the one ormore campaign logic trees.
 11. The system of claim 10, wherein theoperations further comprise: generating an interest map array, whichmaps each interest tag of a plurality of interest tags included in theone or more user profiles to an operand value of the operand valuearray.
 12. The system of claim 9, wherein transforming the one or moreuser profiles into the pre-processed GPU-specialized user data structurefurther comprises: for each user profile of the one or more userprofiles: storing a variable value for each interest tag in an array,the variable value indicating whether or not the interest tag isincluded in the user profile, and the variable value for each userprofile of the one or more user profiles being stored in the array in aninterleaved manner.
 13. The system of claim 8, wherein the offloadingcondition is satisfied when a user profile of the one or more userprofiles includes a threshold number of interest tags.
 14. The system ofclaim 8, wherein the operations further comprise: segmenting a set ofthreads of the GPU into one or more warps of the GPU; segmenting a setof user profiles into one or more subsets of the set of user profiles;and assigning a subset of the set of user profiles to a warp of the oneor more warps of the GPU, wherein when the GPU performs the process, theGPU simultaneously evaluates each user profile of the subset of userprofiles assigned to the warp against a campaign logic tree of the oneor more campaign logic tree.
 15. A computer-program product tangiblyembodied in a non-transitory machine-readable storage medium, includinginstructions configured to cause a processing apparatus to performoperations including: accessing one or more user profiles stored at auser profile database, each user profile of the one or more userprofiles including one or more interest tags, each interest tag of theone or more interest tags characterizing an interest of a user detectedfrom an interaction between a user device and a web server or anapplication server; accessing one or more campaign logic trees, eachcampaign logic tree of the one or more campaign logic trees includingone or more nodes representing logic for determining whether a userprofile satisfies the campaign logic tree, and each node of the one ormore nodes corresponding to an operand value or an operator; for eachuser profile of the one or more user profiles: evaluating one or morecharacteristics of the user profile using an offloading condition, theevaluation of the one or more characteristics generating a resultindicating whether or not the user profile satisfies the offloadingcondition; when the result indicates that the user profile does notsatisfy the offloading condition, using the CPU to perform a process fordetermining whether or not the user profile satisfies each campaignlogic tree of the one or more campaign logic trees; and when the resultindicates that the user profile satisfies the offloading condition,assigning to the GPU a task of performing the process for determiningwhether or not the user profile satisfies each campaign logic tree ofthe one or more campaign logic trees; and generating an output datastructure including at least one user profile of one or more userprofiles, wherein the at least one user profile satisfies at least onecampaign logic tree of the one or more campaign logic trees.
 16. Thecomputer-program product of claim 15, wherein the operations furthercomprise: transforming the one or more campaign logic trees into apre-processed GPU-specialized campaign data structure configured forprocessing by the GPU; and transforming the one or more user profilesinto a pre-processed GPU-specialized user data structure configured forprocessing by the GPU.
 17. The computer-program product of claim 16,wherein transforming the one or more campaign logic trees into thepre-processed GPU-specialized campaign data structure further comprises:transforming the one or more campaign logic trees into a global campaignarray including only one or more operand values and one or moreoperators of each campaign logic tree of the one or more campaign logictrees; and transforming the global campaign array into: an operand valuearray including the one or more operand values of each campaign logictree of the one or more campaign logic trees; and an operator arrayincluding the one or more operators for each campaign logic tree of theone or more campaign logic trees.
 18. The computer-program product ofclaim 17, wherein the operations further comprise: generating aninterest map array, which maps each interest tag of a plurality ofinterest tags included in the one or more user profiles to an operandvalue of the operand value array.
 19. The computer-program product ofclaim 16, wherein transforming the one or more user profiles into thepre-processed GPU-specialized user data structure further comprises: foreach user profile of the one or more user profiles: storing a variablevalue for each interest tag in an array, the variable value indicatingwhether or not the interest tag is included in the user profile, and thevariable value for each user profile of the one or more user profilesbeing stored in the array in an interleaved manner.
 20. Thecomputer-program product of claim 15, wherein the offloading conditionis satisfied when a user profile of the one or more user profilesincludes a threshold number of interest tags.